U.S. patent application number 16/461428 was filed with the patent office on 2019-11-14 for intensity corrected magnetic resonance images.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to MARTIN BERGTHOLDT, FRANK OLAF THIELE, FABIAN WENZEL.
Application Number | 20190346526 16/461428 |
Document ID | / |
Family ID | 57345796 |
Filed Date | 2019-11-14 |
United States Patent
Application |
20190346526 |
Kind Code |
A1 |
WENZEL; FABIAN ; et
al. |
November 14, 2019 |
INTENSITY CORRECTED MAGNETIC RESONANCE IMAGES
Abstract
The invention provides for a medical instrument (100) comprising
a processor (134) and a memory (138) containing machine executable
instructions (140). Execution of the machine executable
instructions causes the processor to: receive (200) a first
magnetic resonance image data set (146) descriptive of a first
region of interest (122) of a subject (118) and receive (202) at
least one second magnetic resonance image data set (152, 152')
descriptive of a second region of interest (124) of the subject.
The first region of interest at least partially comprises the
second region of interest. Execution of the machine executable
instructions further cause the processor to receive (204) an
analysis region (126) within both the first region of interest and
within the second region of interest. Execution of the machine
executable instructions further cause the processor to construct
(206) a cost function comprising an intra-scan homogeneity measure
separately for the first magnetic resonance image data set and
separately for each of the at least one second magnetic resonance
image data set. The cost function further comprises an inter-scan
similarity measure calculated using both the first magnetic
resonance image data set and each of the at least one second
magnetic resonance image data set. Execution of the machine
executable instructions further cause the processor to by
performing an optimization (208) of the cost function by
calculating a first intensity correction map (154) for the first
magnetic resonance image data set using an intensity correction
algorithm within the analysis region and at least one second
intensity correction map (156) for each of the at least one second
magnetic resonance image data set within the analysis region.
Execution of the machine executable instructions further cause the
processor to calculate (210) a first corrected magnetic resonance
image (158) descriptive of the analysis region using the first
magnetic resonance image data set and the first intensity
correction map. Execution of the machine executable instructions
further cause the processor to calculate (212) at least one second
corrected magnetic resonance image (160) descriptive of the
analysis region using the at least at least one second magnetic
resonance image data set and the at least one second intensity
correction map.
Inventors: |
WENZEL; FABIAN; (HAMBURG,
DE) ; BERGTHOLDT; MARTIN; (HAMBURG, DE) ;
THIELE; FRANK OLAF; (AACHEN, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
EINDHOVEN |
|
NL |
|
|
Family ID: |
57345796 |
Appl. No.: |
16/461428 |
Filed: |
November 10, 2017 |
PCT Filed: |
November 10, 2017 |
PCT NO: |
PCT/EP2017/078833 |
371 Date: |
May 16, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/30 20170101; G01R
33/5659 20130101; G06T 7/97 20170101; G01R 33/56 20130101; G06T
5/50 20130101; G01R 33/565 20130101; G06T 2207/10088 20130101; G01R
33/56572 20130101 |
International
Class: |
G01R 33/565 20060101
G01R033/565; G06T 5/50 20060101 G06T005/50; G06T 7/30 20060101
G06T007/30; G06T 7/00 20060101 G06T007/00 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 17, 2016 |
EP |
16199279.7 |
Claims
1. A medical instrument comprising a processor and a memory,
wherein the memory contains machine executable instructions,
wherein execution of the machine executable instructions causes the
processor to: receive a first magnetic resonance image data set
descriptive of a first region of interest of a subject; receive at
least one second magnetic resonance image data set descriptive of a
second region of interest of the subject, wherein the first region
of interest at least partially comprises the second region of
interest; receive an analysis region within both the first region
of interest and within the second region of interest; construct a
cost function comprising an intra-scan homogeneity measure
separately for the first magnetic resonance image data set and
separately for each of the at least one second magnetic resonance
image data set, wherein the cost function further comprises an
inter-scan similarity measure calculated using both the first
magnetic resonance image data set and each of the at least one
second magnetic resonance image data set; by performing an
optimization of the cost function by calculating a first intensity
correction map for the first magnetic resonance image data set
using an intensity correction algorithm within the analysis region
and at least one second intensity correction map for each of the at
least one second magnetic resonance image data set within the
analysis region; calculate a first corrected magnetic resonance
image descriptive of the analysis region using the first magnetic
resonance image data set and the first intensity correction map;
and calculate at least one second corrected magnetic resonance
image descriptive of the analysis region using the at least at
least one second magnetic resonance image data set and the at least
one second intensity correction map.
2. The medical instrument of claim 1, wherein the medical
instrument comprises a first magnetic resonance imaging system,
wherein execution of the machine executable instructions further
causes the processor to acquire the first magnetic resonance image
data set by controlling the first magnetic resonance imaging
system.
3. The medical instrument of claim 2, wherein the medical
instrument comprises a second magnetic resonance imaging system,
wherein execution of the machine executable instructions further
causes the processor to acquire at least a portion of the at least
one second magnetic resonance image data set by controlling the
second magnetic resonance imaging system.
4. The medical instrument of claim 1, wherein execution of the
machine executable instructions further causes the processor to
register each of the at least one second magnetic resonance image
data set to the first magnetic resonance image data set during
optimization of the cost function.
5. The medical instrument of claim 4, wherein registration of each
of the at least one second magnetic resonance image data set to the
first magnetic resonance image data set, calculation of the first
intensity correction map, and calculation of the at least one
second intensity correction map are all performed as a joint
optimization.
6. The medical instrument of claim 1, wherein the inter-scan
similarity measure comprises a term measuring similarity between
the first magnetic resonance image data set and each of the second
magnetic resonance image data set.
7. The medical instrument of claim 6, wherein the inter-scan
similarity measure comprises a term measuring the similarity
between each second magnetic resonance image data set.
8. The medical instrument of claim 1, wherein execution of the
machine executable instructions further causes the processor to
perform a longitudinal analysis of the first corrected magnetic
resonance image and the at least second corrected magnetic
resonance image.
9. The medical instrument of claim 1, wherein the inter-scan
similarity measure is a maximized mutual information algorithm.
10. The medical instrument of claim 1, wherein the inter-scan
similarity measure comprises a voxel wise sum of squared
differences.
11. The medical instrument of claim 1, wherein the inter-scan
similarity measure comprises a measure of image
cross-correlation.
12. The medical instrument of claim 1, wherein the intensity
correction algorithm is any one of the following: a b-splines bias
field correction algorithm, a DCT coefficients bias field
correction algorithm, and polynomial bias field correction
algorithm.
13. A method of medical imaging, wherein the method comprises:
receiving a first magnetic resonance image data set descriptive of
a first region of interest of a subject; receiving at least one
second magnetic resonance image data set descriptive of a second
region of interest of the subject, wherein the first region of
interest at least partially comprises the second region of
interest; receiving an analysis region within both the first region
of interest and within the second region of interest; constructing
a cost function comprising an intra-scan homogeneity measure
separately for the first magnetic resonance image data set and
separately for each of the at least one second magnetic resonance
image data set, wherein the cost function further comprises an
inter-scan similarity measure calculated using both the first
magnetic resonance image data set and each of the at least one
second magnetic resonance image data set; performing an
optimization of the cost function by calculating a first intensity
correction map for the first magnetic resonance image data set
using an intensity correction algorithm within the analysis region
and at least one second intensity correction map for each of the at
least one second magnetic resonance image data set within the
analysis region; calculating a first corrected magnetic resonance
image descriptive of the analysis region using the first magnetic
resonance image data set and the first intensity correction map;
and calculating at least one second corrected magnetic resonance
image descriptive of the analysis region using the at least at
least one second magnetic resonance image data set and the at least
one second intensity correction map.
14. The method of claim 13, wherein the method comprises acquiring
the first magnetic resonance image data set with a first magnetic
resonance imaging system, and wherein the method comprises
acquiring the second magnetic resonance image data set with a first
magnetic resonance imaging system.
15. A computer program product comprising machine executable
instructions stored on a non-transitory computer readable medium
for execution by a processor controlling a medical instrument,
wherein execution of the machine executable instructions causes the
processor to: receive a first magnetic resonance image data set
descriptive of a first region of interest of a subject; receive at
least one second magnetic resonance image data set descriptive of a
second region of interest of the subject, wherein the first region
of interest at least partially comprises the second region of
interest; receive an analysis region within both the first region
of interest and within the second region of interest; construct a
cost function comprising an intra-scan homogeneity measure
separately for the first magnetic resonance image data set and
separately for each of the at least one second magnetic resonance
image data set, wherein the cost function further comprises an
inter-scan similarity measure calculated using both the first
magnetic resonance image data set and each of the at least one
second magnetic resonance image data set; by performing an
optimization of the cost function by calculating a first intensity
correction map for the first magnetic resonance image data set
using an intensity correction algorithm within the analysis region
and at least one second intensity correction map for each of the at
least one second magnetic resonance image data set within the
analysis region; calculate a first corrected magnetic resonance
image descriptive of the analysis region using the first magnetic
resonance image data set and the first intensity correction map;
and calculate at least one second corrected magnetic resonance
image descriptive of the analysis region using the at least at
least one second magnetic resonance image data set and the at least
one second intensity correction map.
Description
FIELD OF THE INVENTION
[0001] The invention relates to magnetic resonance imaging, in
particular to the correction of intensity inhomogeneities for a
series of magnetic resonance images.
BACKGROUND OF THE INVENTION
[0002] A large static magnetic field is used by Magnetic Resonance
Imaging (MRI) scanners to align the nuclear spins of atoms as part
of the procedure for producing images within the body of a patient.
This large static magnetic field is referred to as the BO field or
the main magnetic field.
[0003] One method of spatially encoding is to use magnetic field
gradient coils. Typically there are three coils which are used to
generate three different gradient magnetic fields in three
different orthogonal directions.
[0004] During an MRI scan, Radio Frequency (RF) pulses generated by
one or more transmitter coils cause a called B1 field. Additionally
applied gradient fields and the B1 field do cause perturbations to
the effective local magnetic field. RF signals are then emitted by
the nuclear spins and detected by one or more receiver coils. The
receiver coils typically have a sensitivity which is spatially
dependent. This spatial dependency is one factor which can lead to
intensity inhomogeneities in magnetic resonance images. Spatially
dependent intensity inhomogeneities are often referred to as bias
field inhomogeneity (or simply bias field) or signal inhomogeneity.
Bias field inhomogeneities can in particular cause difficulties
when using automatic algorithms to segment or identify regions in
magnetic resonance images.
[0005] A variety of techniques exist for bias field correction. For
example International patent application WO 2016/042037 A1
discloses a method of bias correction and image registration. Each
image including a bias in intensity within the image of unknown
magnitude, is performed by: a) inputting a digital data set of a
first image and a digital data set of a second image into a
computer; b) calculating a deformation of said first image that
transforms said first image into a transformed image that is an
optimized approximation of said second image and c) simultaneously
calculating and applying a bias correction which is applied to said
first image and a bias correction which is applied to said
transformed image such that each of the first image and the
transformed image is individually corrected for bias therein.
Generally, an average of the bias correction over the first image
is equal and opposite to an average of the bias correction over
said transformed image.
SUMMARY OF THE INVENTION
[0006] The invention provides for a medical instrument, a method,
and a computer program product in the independent claims.
Embodiments are given in the dependent claims.
[0007] Embodiments of the invention may provide a means for
providing consistent bias field or signal intensity correction for
a series of magnetic resonance images. Herein the series of images
is referred to as a first magnetic resonance data set and at least
one second magnetic resonance image data set. There is a first
magnetic resonance image (the a first magnetic resonance data set)
and one or more second magnetic resonance images (at least one
second magnetic resonance image data set).
[0008] This signal intensity correction is performed as part of an
optimization process. Embodiments may achieve this by optimizing a
cost function. The cost function may comprise several different
factors. For each image in the series there is a term which
represents an intra-scan homogeneity measure. The intra-scan
homogeneity measure is a measure of how uniform the intensity is
within one image. The cost function also comprises additional terms
which comprise an inter-scan similarity measure. The inter-scan
similarity measure is an algorithm which compares the first
magnetic resonance image with each of the second magnetic resonance
images.
[0009] Examples of inter-scan similarity measures can for example
be found in the manual for the software package elastix from the
University of Utrecht in the Netherlands. See Stefan Klein and
Marius Staring, Elastix the manual, Sep. 4, 2015 pages 6 to 7 in
chapter 2.3 Metrics. This manual is available online at
http://elastix.isi.uu.nl/doxygen/index.html.
[0010] During the optimization, a standard intensity correction
algorithm or bias field correction algorithm is applied to all of
the images. The optimization of the cost function causes the
intensity correction to be applied in a way that not only corrects
intensity inhomogeneities within a single magnetic resonance image,
but also make the intensity profile of the entire series of
magnetic resonance images converge.
[0011] This may have huge advantages when performing so called
longitudinal studies where a series of magnetic resonance images
from different times are examined. Making the intensity profiles of
the magnetic resonance images more uniform may also increases the
ability of automatic algorithms to correctly segment or analyze the
series of magnetic resonance images.
[0012] In one aspect the invention provides for a medical
instrument comprising a processor and a memory. The memory contains
machine-executable instructions for execution by the processor.
Execution of the machine-executable instructions causes the
processor to receive a first magnetic resonance image dataset
descriptive of a first region of interest of a subject. The first
magnetic resonance image dataset may be three-dimensional magnetic
resonance data, a collection of two-dimensional slabs or slices of
magnetic resonance image data and in some cases may also be a
single slice of magnetic resonance image data. The first magnetic
resonance image dataset may be data which may be rendered in a
two-dimensional or three-dimensional format to illustrate or show
the first region of interest of the subject.
[0013] The receiving of the first magnetic resonance image dataset
may be performed in several different ways. In some instances the
final reconstructed first magnetic resonance image dataset is
received in image space. In other examples the first magnetic
resonance image dataset may be received by receiving magnetic
resonance data that is reconstructed into image space. In yet other
instances the receiving of the first magnetic resonance image
dataset may also be performed by controlling a magnetic resonance
imaging system to acquire magnetic resonance data which is then
reconstructed into image space resulting in the first magnetic
resonance image dataset.
[0014] Execution of the machine-executable instructions further
cause a processor to receive at least one second magnetic resonance
image dataset descriptive of a second region of interest of the
subject. The details describing how the first magnetic resonance
image dataset can be received are also applicable to the at least
one second magnetic resonance image dataset. The first region of
interest at least partially comprises the second region of
interest. The first magnetic resonance image dataset and each of
the at least one second magnetic resonance image dataset may be
acquired using the same magnetic resonance imaging system or using
different magnetic resonance imaging systems.
[0015] When a subject is placed into a magnetic resonance imaging
system the exact anatomical region or region of interest which is
imaged may not be identical every single time. The second region of
interest and the first region of interest may therefore not be
completely identical. Also the subject may be in a slightly
different position such that although the same anatomical region
may be imaged the area or the positioning of the subject within the
region of interest may be slightly different in each case.
[0016] Execution of the machine-executable instructions further
causes the processor to receive an analysis region within both the
first region of interest and within the second region of interest.
The analysis region is essentially a region where the first region
of interest and the second region of interest overlap. The analysis
region is a common space to both regions of interest. The receiving
of the analysis region may be received for example by a physician
or other medical technologist indicating the region within each of
the first magnetic resonance imaging dataset and the at least one
second magnetic resonance imaging dataset. In other instances the
receiving of the analysis region may be performed automatically by
for example performing a registration between the first magnetic
resonance image dataset and the at least one second magnetic
resonance imaging dataset to indicate which portions of these
datasets correspond to each other and how the data may overlap.
[0017] Execution of the machine-executable instructions further
cause the processor to construct a cost function comprising an
intra-scan homogeneity measure separately for the first magnetic
resonance imaging dataset and separately for the at least one
second magnetic resonance image dataset. The intra-scan homogeneity
measure is an algorithm which measures the homogeneous contrast of
an image. In magnetic resonance images there can be differences in
the contrast across a magnetic resonance image that are not due to
the physical characteristics of the subject but are due to for
example the characteristics of the magnetic resonance imaging
system itself, for example in the sensitivity of the
radio-frequency coils. The intra-scan homogeneity measure is put in
a cost function to indicate how uniform the homogeneity measure is
and to try to quantify inhomogeneities within a magnetic resonance
image itself.
[0018] The cost function further comprises an inter-scan similarity
measure calculated both the first magnetic resonance image dataset
and each of the at least one second magnetic resonance image
dataset. The cost function further has the inter-scan similarity
measure to compare the first magnetic resonance image dataset to
each of the at least one second magnetic resonance image datasets.
The cost function is therefore dependent upon not just the
intra-scan homogeneity but also a comparison between the different
images. In some instances the first magnetic resonance image
dataset may be registered to the at least one second magnetic
resonance image dataset. In this case the cost function could be
used simply to remove inhomogeneities intensities across all of the
magnetic resonance imaging datasets. In other cases the various
magnetic resonance image datasets may not be registered to each
other. The cost function as it compares the inter-scan similarity
measure may also be used as part of a registration process between
the various datasets.
[0019] Execution of the machine-executable instructions further
cause a processor to perform an optimization of the cost function
to calculate a first intensity correction map for the first
magnetic resonance image dataset using an intensity correction
algorithm within the analysis region and at least one second
intensity correction map for each of the at least one second
magnetic resonance imaging dataset within the analysis region.
Various algorithms for calculating intensity correction maps for a
magnetic resonance imaging system are known. For example there are
various models which are used for the so called bias-field
correction. A bias-field correction algorithm such as is used for
B-splines, DCT coefficients, or polynomial fields are just several
examples.
[0020] Execution of the machine-executable instructions further
cause a processor to calculate a first corrected magnetic resonance
image descriptive of the analysis region using the first magnetic
resonance image dataset and the first intensity correction map. In
this step the first intensity correction map is applied to the
first corrected magnetic resonance image dataset and this may be
used to produce a corrected first magnetic resonance image dataset
or even to render the first corrected magnetic resonance image.
Execution of the machine-executable instructions further cause the
processor to calculate at least one second corrected magnetic
resonance image descriptive of the analysis region using the at
least one second magnetic resonance image dataset and the at least
one second intensity correction map. The at least one second
intensity correction map may be used to generate corrected magnetic
resonance data or even to render corrected magnetic resonance
images for the at least one second magnetic resonance image
dataset.
[0021] The advantages of this embodiment may be that because more
than one magnetic resonance image dataset is being compared for the
same anatomical region of the subject, this additional information
may lead to better correction of intensity inhomogeneities such as
bias-field correction than if each individual image is examined
alone. Additionally, because the first magnetic resonance image
dataset and the at least one second magnetic resonance image
dataset have been compared and optimized for inter-scan similarity
these resulting magnetic resonance images may be better used for
automatic comparison of anatomical features within a subject. For
example if a tumor is being examined over multiple magnetic
resonance examinations the matching contrast within the images may
allow an automatic algorithm to identify the location and/or size
of a tumor more uniformly within the resulting images.
[0022] In another embodiment the step of receiving an analysis
region within both the first region of interest and within the
second region of interest comprises receiving a registration
between the first magnetic resonance image dataset and at least
each of the at least one second magnetic resonance imaging dataset.
This registration may in some cases be performed well enough to
enable all of the image processing. In other examples this may be a
preliminary registration which is then refined during the
optimization of the cost function.
[0023] In another embodiment, the medical instrument comprises a
first magnetic resonance imaging system. Execution of the
machine-executable instructions further cause the processor to
acquire the first magnetic resonance image dataset by controlling
the first magnetic resonance imaging system. The step of receiving
the first magnetic resonance image dataset may also comprise
reconstructing data acquired from k-space into image space.
[0024] In another embodiment, the medical instrument further
comprises a second magnetic resonance imaging system. Execution of
the machine-executable instructions further cause a processor to
acquire at least a portion of the at least one second magnetic
resonance image dataset by controlling the second magnetic
resonance imaging system. Again, the receiving of the second
magnetic resonance image dataset may also comprise reconstructing
the at least one second magnetic resonance image dataset from data
that was acquired in k-space.
[0025] A further advantage of the embodiments may be that the
optimization of the cost function more easily enables images
acquired on different magnetic resonance imaging systems to be
compared. Another advantage may be that the optimization of the
cost function may enable magnetic resonance images acquired at
different periods of time with the same magnetic resonance imaging
system to be more easily compared. For example comparing magnetic
resonance images acquired days, months or even years apart.
[0026] In another embodiment, execution of the machine-executable
instructions further causes the processor to register each of the
at least one second magnetic resonance image dataset to the first
magnetic resonance image dataset during optimization of the cost
function. In this embodiment instead of just having a term in the
cost function between the first magnetic resonance imaging dataset
and individual second magnetic resonance image datasets there are
now terms between the individual second magnetic resonance image
datasets. This may be beneficial as it may result in a better
optimization of the cost function. This may however come with the
cost of increasing computational costs.
[0027] In another embodiment, the registration of each of the at
least one second magnetic resonance image data set to the first
magnetic resonance image data set, by calculation of the first
intensity correction map, and calculation of the at least one
second intensity correction map are all performed as a joint
optimization. For example, the registration can use the inter-scan
similarity measure to perform a registration or a refinement of an
existing registration. This may be beneficial because
non-uniformity of the contrast within the magnetic resonance image
datasets may cause errors in performing an initial registration.
Performing a joint optimization may enable both better registration
between images and better correction for the bias field
inhomogeneity than could be possible if the two are performed
separately.
[0028] In another embodiment, the inter-scan similarity measure
comprises a term measuring similarity between the first magnetic
resonance image dataset and each of the second magnetic resonance
image dataset. The inter-scan similarity measure could be an
algorithm that compares the corresponding voxels of the different
magnetic resonance image datasets. This may be beneficial in
assuring uniformity of the contrast of multiple images which are
possibly acquired at different times and locations.
[0029] In another embodiment, the inter-scan similarity measure
comprises a term measuring the similarity between each of the
second magnetic resonance image dataset. In this example the
inter-scan similarity measure is expanded to also include a
comparison between the various second magnetic resonance image
datasets. This may be beneficial in assuring that the images across
all that have been acquired are more uniform.
[0030] In another embodiment, execution of the machine-executable
instructions further cause the processor to perform a longitudinal
analysis of the first corrected magnetic resonance image and the at
least second corrected magnetic resonance image. A longitudinal
analysis is when a series of magnetic resonance images is compared.
This embodiment may be beneficial because the process of optimizing
the cost function has made the various images more uniform with
respect to each other. Automatic algorithms may perform more
consistently on all the images.
[0031] In another embodiment, the intra-scan homogeneity measure is
a measure of intensity homogeneity. Examples of a measure of
intensity homogeneity may for example be such things as using
histogram sharpening. Image intensities may for example be assumed
to belong to mixed Gaussian distributions. The inhomogeneity may
therefore be related to the standard deviation.
[0032] In another embodiment, the inter-scan homogeneity measure is
a maximized mutual information algorithm. A maximized mutual
information algorithm is a standard image processing algorithm used
to compare two images or image datasets. This may be beneficial in
comparing the multiple magnetic resonance image datasets.
[0033] In another embodiment, the first magnetic resonance image
dataset and the at least one second magnetic resonance image
dataset comprise magnetic resonance data acquired using different
pulse sequence commands. For example the first magnetic resonance
image dataset and the at least one second magnetic resonance image
dataset may be acquired using different magnetic resonance imaging
protocols. The use of for example the maximized mutual information
algorithm may still enable the images to be compared effectively
within a cost function even though the different images may have
different inherent contrast characteristics.
[0034] In another embodiment, the inter-scan similarity measure
comprises a voxel by voxel comparison using a sum of squared
differences algorithm to compare intensities. In this embodiment,
the various magnetic resonance datasets are compared voxel-by-voxel
and a squared difference algorithm is used to compare them.
[0035] In another embodiment, the inter-scan similarity measure
comprises a measure of image cross correlation. A cross correlation
algorithm is a standard image processing technique which may be
used for comparing different images.
[0036] In another embodiment, the intensity correction algorithm is
any one of the following: a B-spline bias-field correction
algorithm, a DCT coefficients bias-field correction algorithm, and
a polynomial bias-field correction algorithm. The use of any of
these or other standard intensity correction algorithms may be
effectively used within the optimization of the cost function.
[0037] In another aspect, the invention provides for a method of
medical imaging. The method comprises receiving a first magnetic
resonance image dataset descriptive of a first region of interest
of a subject. The method further comprises receiving at least one
second magnetic resonance image dataset descriptive of a second
region of interest of the subject. The first region of interest at
least partially comprises the second region of interest. The method
further comprises receiving an analysis region within both the
first region of interest and within the second region of interest.
The method further comprises constructing a cost function
comprising an intra-scan homogeneity measure separately for the
first magnetic resonance image dataset and separately each of the
at least one second magnetic resonance image dataset. The cost
function further comprises an inter-scan similarity measure
calculated using both the first magnetic resonance image dataset
and each of the at least one second magnetic resonance image
dataset.
[0038] The method further comprises performing an optimization of
the cost function to calculate a first intensity correction map for
the first magnetic resonance image dataset using an intensity
correction algorithm within the analysis region and at least one
second intensity correction map for each of the at least one second
magnetic resonance image dataset within the analysis region. The
method further comprises calculating a first corrected magnetic
resonance image descriptive of the analysis region using the first
magnetic resonance image dataset and the first intensity correction
map. The method further comprises calculating the at least one
second corrected magnetic resonance image descriptive of the
analysis region using the at least one second magnetic resonance
image dataset and the at least one second intensity correction
map.
[0039] In another embodiment, the method further comprises
acquiring the first magnetic resonance image dataset with a first
magnetic resonance imaging system.
[0040] In another embodiment, the method further comprises
acquiring the at least one second magnetic resonance image dataset
with a second magnetic resonance imaging system.
[0041] In another aspect, the invention provides for a computer
program product comprising machine-executable instructions for
execution by a processor controlling the medical instrument.
Execution of the machine-executable instructions cause the
processor to receive a first magnetic resonance image dataset
descriptive of a first region of interest of a subject. Execution
of the machine-executable instructions further cause the processor
to receive the at least one second magnetic resonance image dataset
descriptive of a second region of interest of the subject. The
first region of interest at least partially comprises the second
region of interest. Execution of the machine-executable
instructions further cause the processor to receive an analysis
region within both the first region of interest and within the
second region of interest. Execution of the machine-executable
instructions further cause the processor to construct a cost
function comprising an intra-scan homogeneity measure separately
for the first magnetic resonance image dataset and separately for
each of the at least one second magnetic resonance image
dataset.
[0042] The cost function further comprises an inter-scan similarity
measure calculated using both the first magnetic resonance image
dataset and each of the at least one second magnetic resonance
image dataset. Execution of the machine-executable instructions
further cause the processor to perform an optimization of the cost
function to calculate a first intensity correction map for the
first magnetic resonance image dataset using an intensity
correction algorithm within the analysis region and at least one
second intensity correction map for each of the at least one second
magnetic resonance image dataset within the analysis region.
Execution of the machine-executable instructions further cause the
processor to calculate a first corrected magnetic resonance image
descriptive of the analysis region using the first magnetic
resonance image dataset and the first intensity correction map.
[0043] Execution of the machine-executable instructions further
cause the processor to calculate the at least one second corrected
magnetic resonance image descriptive of the analysis region using
the at least one second magnetic resonance image dataset and the at
least one second intensity correction map.
[0044] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as an apparatus, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
executable code embodied thereon.
[0045] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
`computer-readable storage medium` as used herein encompasses any
tangible storage medium which may store instructions which are
executable by a processor of a computing device. The
computer-readable storage medium may be referred to as a
computer-readable non-transitory storage medium. The
computer-readable storage medium may also be referred to as a
tangible computer readable medium. In some embodiments, a
computer-readable storage medium may also be able to store data
which is able to be accessed by the processor of the computing
device. Examples of computer-readable storage media include, but
are not limited to: a floppy disk, a magnetic hard disk drive, a
solid state hard disk, flash memory, a USB thumb drive, Random
Access Memory (RAM), Read Only Memory (ROM), an optical disk, a
magneto-optical disk, and the register file of the processor.
Examples of optical disks include Compact Disks (CD) and Digital
Versatile Disks (DVD), for example CD-ROM, CD-RW, CD-R, DVD-ROM,
DVD-RW, or DVD-R disks. The term computer readable-storage medium
also refers to various types of recording media capable of being
accessed by the computer device via a network or communication
link. For example a data may be retrieved over a modem, over the
internet, or over a local area network. Computer executable code
embodied on a computer readable medium may be transmitted using any
appropriate medium, including but not limited to wireless, wire
line, optical fiber cable, RF, etc., or any suitable combination of
the foregoing.
[0046] A computer readable signal medium may include a propagated
data signal with computer executable code embodied therein, for
example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0047] `Computer memory` or `memory` is an example of a
computer-readable storage medium. Computer memory is any memory
which is directly accessible to a processor. `Computer storage` or
`storage` is a further example of a computer-readable storage
medium. Computer storage may be any volatile or non-volatile
computer-readable storage medium.
[0048] A `processor` as used herein encompasses an electronic
component which is able to execute a program or machine executable
instruction or computer executable code. References to the
computing device comprising "a processor" should be interpreted as
possibly containing more than one processor or processing core. The
processor may for instance be a multi-core processor. A processor
may also refer to a collection of processors within a single
computer system or distributed amongst multiple computer systems.
The term computing device should also be interpreted to possibly
refer to a collection or network of computing devices each
comprising a processor or processors. The computer executable code
may be executed by multiple processors that may be within the same
computing device or which may even be distributed across multiple
computing devices.
[0049] Computer executable code may comprise machine executable
instructions or a program which causes a processor to perform an
aspect of the present invention. Computer executable code for
carrying out operations for aspects of the present invention may be
written in any combination of one or more programming languages,
including an object oriented programming language such as Java,
Smalltalk, C++ or the like and conventional procedural programming
languages, such as the C programming language or similar
programming languages and compiled into machine executable
instructions. In some instances the computer executable code may be
in the form of a high level language or in a pre-compiled form and
be used in conjunction with an interpreter which generates the
machine executable instructions on the fly.
[0050] The computer executable code may execute entirely on the
user's computer, partly on the user's computer, as a stand-alone
software package, partly on the user's computer and partly on a
remote computer or entirely on the remote computer or server. In
the latter scenario, the remote computer may be connected to the
user's computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider).
[0051] Aspects of the present invention are described with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It is understood that
each block or a portion of the blocks of the flowchart,
illustrations, and/or block diagrams, can be implemented by
computer program instructions in form of computer executable code
when applicable. It is further understood that, when not mutually
exclusive, combinations of blocks in different flowcharts,
illustrations, and/or block diagrams may be combined. These
computer program instructions may be provided to a processor of a
general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the
computer or other programmable data processing apparatus, create
means for implementing the functions/acts specified in the
flowchart and/or block diagram block or blocks.
[0052] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0053] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0054] A `user interface` as used herein is an interface which
allows a user or operator to interact with a computer or computer
system. A `user interface` may also be referred to as a `human
interface device.` A user interface may provide information or data
to the operator and/or receive information or data from the
operator. A user interface may enable input from an operator to be
received by the computer and may provide output to the user from
the computer. In other words, the user interface may allow an
operator to control or manipulate a computer and the interface may
allow the computer indicate the effects of the operator's control
or manipulation. The display of data or information on a display or
a graphical user interface is an example of providing information
to an operator. The receiving of data through a keyboard, mouse,
trackball, touchpad, pointing stick, graphics tablet, joystick,
gamepad, webcam, headset, pedals, wired glove, remote control, and
accelerometer are all examples of user interface components which
enable the receiving of information or data from an operator.
[0055] A `hardware interface` as used herein encompasses an
interface which enables the processor of a computer system to
interact with and/or control an external computing device and/or
apparatus. A hardware interface may allow a processor to send
control signals or instructions to an external computing device
and/or apparatus. A hardware interface may also enable a processor
to exchange data with an external computing device and/or
apparatus. Examples of a hardware interface include, but are not
limited to: a universal serial bus, IEEE 1394 port, parallel port,
IEEE 1284 port, serial port, RS-232 port, IEEE-488 port, bluetooth
connection, wireless local area network connection, TCP/IP
connection, ethernet connection, control voltage interface, MIDI
interface, analog input interface, and digital input interface.
[0056] A `display` or `display device` as used herein encompasses
an output device or a user interface adapted for displaying images
or data. A display may output visual, audio, and or tactile data.
Examples of a display include, but are not limited to: a computer
monitor, a television screen, a touch screen, tactile electronic
display, Braille screen, Cathode ray tube (CRT), Storage tube,
Bi-stable display, Electronic paper, Vector display, Flat panel
display, Vacuum fluorescent display (VF), Light-emitting diode
(LED) display, Electroluminescent display (ELD), Plasma display
panel (PDP), Liquid crystal display (LCD), Organic light-emitting
diode display (OLED), a projector, and Head-mounted display.
[0057] Magnetic Resonance (MR) data is defined herein as being the
recorded measurements of radio frequency signals emitted by atomic
spins using the antenna of a magnetic resonance apparatus during a
magnetic resonance imaging scan. Magnetic resonance data is an
example of medical imaging data. A Magnetic Resonance (MR) image is
defined herein as being the reconstructed two or three dimensional
visualization of anatomic data contained within the magnetic
resonance imaging data. A magnetic resonance image comprises
voxels. Voxels themselves represent an average of the magnetic
resonance data for a defined volume. A two dimensional collection
of voxels therefore resembles an image which is representative of a
slice of the object being imaged. Two dimensional collections of
voxels are therefore often referred to as a "slice."
[0058] A magnetic resonance image data set herein is understood to
be a magnetic resonance image. The magnetic resonance image data
set can be a three dimensional data set, a collection (or stack) of
two dimensional slices, or a single two dimensional slice.
[0059] It is understood that one or more of the aforementioned
embodiments of the invention may be combined as long as the
combined embodiments are not mutually exclusive.
BRIEF DESCRIPTION OF THE DRAWINGS
[0060] In the following preferred embodiments of the invention will
be described, by way of example only, and with reference to the
drawings in which:
[0061] FIG. 1 illustrates an example of a medical imaging
system;
[0062] FIG. 2 shows a flow chart which illustrates a method of
operating the medical imaging system of FIG. 1; and
[0063] FIG. 3 shows several figures which are used to ideally
represent a series of magnetic resonance images.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0064] Like numbered elements in these figures are either
equivalent elements or perform the same function. Elements which
have been discussed previously will not necessarily be discussed in
later figures if the function is equivalent.
[0065] FIG. 1 illustrates an example of a medical instrument 100.
In this example the medical instrument comprises a magnetic
resonance imaging system 102 and a computer system 130. In some
examples, the medical instrument 100 only comprises the computer
system 130 or an equivalent controller. The magnetic resonance
imaging system 102 comprises a magnet 104. The magnet 104 is a
superconducting cylindrical type magnet 104 with a bore 106 through
it. The use of different types of magnets is also possible. Inside
the cryostat of the cylindrical magnet, there is a collection of
superconducting coils. Within the bore 106 of the cylindrical
magnet 104 there is an imaging zone 108 where the magnetic field is
strong and uniform enough to perform magnetic resonance
imaging.
[0066] Within the bore 106 of the magnet there is also a set of
magnetic field gradient coils 110 which is used for acquisition of
magnetic resonance data to spatially encode magnetic spins within
the imaging zone 108 of the magnet 104. The magnetic field gradient
coils 110 are connected to a magnetic field gradient coil power
supply 112. The magnetic field gradient coils 110 are intended to
be representative. Typically magnetic field gradient coils 110
contain three separate sets of coils for spatially encoding in
three orthogonal spatial directions. A magnetic field gradient
power supply supplies current to the magnetic field gradient coils.
The current supplied to the magnetic field gradient coils 110 is
controlled as a function of time and may be ramped or pulsed.
[0067] Adjacent to the imaging zone 108 is a radio-frequency coil
114 for manipulating the orientation of magnetic spins within the
imaging zone 108 and for receiving radio transmissions from spins
also within the imaging zone 108. The radio frequency antenna may
contain multiple coil elements. The radio frequency antenna may
also be referred to as a channel or antenna. The radio-frequency
coil 114 is connected to a radio frequency transceiver 116. The
radio-frequency coil 114 and radio frequency transceiver 116 may be
replaced by separate transmit and receive coils and a separate
transmitter and receiver. It is understood that the radio-frequency
coil 114 and the radio frequency transceiver 116 are
representative. The radio-frequency coil 114 is intended to also
represent a dedicated transmit antenna and a dedicated receive
antenna. Likewise the transceiver 116 may also represent a separate
transmitter and receiver. The radio-frequency coil 114 may also
have multiple receive/transmit elements and the radio frequency
transceiver 116 may have multiple receive/transmit channels.
[0068] Within the bore 106 of the magnet 104 there is a subject
support 120 which supports the subject at least partially within in
the imaging zone 108. Within the imaging zone 108 there can be seen
a first region of interest 122 and a second region of interest 124.
For example the subject 118 could be placed into the magnetic
resonance imaging system 102 multiple times. It may be very
difficult to image exactly the same location of the subject 118
every single time. The region marked 126 is an analysis region 126
that is within both the first region of interest 122 and the second
region of interest 124. The analysis region 126 is a region in both
regions of interest 122, 124 that will be corrected for
inhomogeneities. In some examples the first region of interest 122
and the second region of interest 124 could overlap or be
identical. For example a series of magnetic resonance images could
be acquired from the subject 118 for a single examination.
[0069] In other examples, the first region of interest 122 and any
subsequent second region of interest 124 may be located in
different locations as was mentioned previously because the subject
118 has been inserted repeatedly into the magnetic resonance
imaging system 102 at different times. In yet further examples the
first region of interest 122 and subsequent second region of
interest 124 could be within different magnetic resonance imaging
systems altogether.
[0070] The transceiver 116 and the magnetic field gradient coil
power supply 112 can be seen as being connected to a hardware
interface 132 of computer system 130. The computer system further
comprises a processor 134 that is in communication with the
hardware interface 132, a memory 138, and a user interface 136. The
memory 138 (also referred to as computer memory) may be any
combination of memory which is accessible to the processor 134.
This may include such things as main memory, cached memory, and
also non-volatile memory such as flash RAM, hard drives, or other
storage devices. In some examples the memory 134 may be considered
to be a non-transitory computer-readable medium. The memory 134 is
shown as storing machine-executable instructions 140 which enable
the processor 132 to control the operation and function of the
magnetic resonance imaging system 100.
[0071] The computer memory 138 is shown as containing
machine-executable instructions 140 which enable the processor 134
to either control the magnetic resonance imaging system 102 and/or
to perform image processing or data analysis. The computer memory
138 is further shown as containing pulse sequence commands 142 that
enable the processor 134 to control the magnetic resonance imaging
system to acquire magnetic resonance data from the first region of
interest 122 and/or the second region of interest 124.
[0072] Pulse sequence commands as used herein encompass commands or
a timing diagram which may be converted into commands which are
used to control the functions of the magnetic resonance imaging
system 102 as a function of time. Pulse sequence commands are the
implementation of the magnetic resonance imaging protocol applied
to a particular magnetic resonance imaging system 102.
[0073] The computer memory 138 is shown as containing first
magnetic resonance data 144 that was acquired for the first region
of interest 122 and second magnetic resonance data 150 that was
acquired from the second region of interest 124. Both the first
magnetic resonance data and the second magnetic resonance data 150
were acquired by controlling the magnetic resonance imaging system
with the pulse sequence commands 142. The computer memory 138 is
further shown as containing a first magnetic resonance image
dataset 146 that was reconstructed from the first magnetic
resonance data 144. The first magnetic resonance image dataset 146
may for example be a three-dimensional reconstruction of magnetic
resonance image data from the first magnetic resonance data
144.
[0074] The computer memory 138 is further shown as containing a
second magnetic resonance image dataset 152 that was reconstructed
from the second magnetic resonance data 150. The machine-executable
instructions 140 may contain an implementation of an optimization
of a cost function. The optimization of the cost function may be
used to calculate a first intensity correction map 154 for the
first magnetic resonance image dataset 146 and a second intensity
correction map 156 for the second magnetic resonance image dataset
152.
[0075] The computer memory 138 is further shown as containing a
first corrected magnetic resonance image 158 that was calculated by
applying the first intensity correction map 154 to the first
magnetic resonance image dataset 146. The computer memory 138 is
shown as further containing a second corrected magnetic resonance
image 160 that was calculated by applying the second intensity
correction map 156 to the second magnetic resonance image dataset
152. The magnetic resonance images 158 and 160 may for example be
displayed on the user interface 136.
[0076] FIG. 2 shows a flowchart which illustrates a method of
operating the medical imaging system 100 of FIG. 1. First in step
200, the processor 134 receives a first magnetic resonance image
dataset 146. The first magnetic resonance image dataset 146 is
descriptive of a first region of interest 122 of the subject 118.
The receiving of the first magnetic resonance image dataset 146 may
in some cases be the accessing of the first magnetic resonance
image dataset 146 from the computer memory 138. In other examples
the receiving of the first magnetic resonance image dataset may be
the controlling of the magnetic resonance imaging system 102 to
acquire the first magnetic resonance data 144 and then the
reconstruction of the first magnetic resonance data 144 into the
first magnetic resonance image dataset 146.
[0077] Next in step 202, the processor 134 receives at least one
second magnetic resonance image dataset 152. The second magnetic
resonance image dataset 152 is for a second region of interest 124.
The receiving of the at least one second magnetic resonance image
dataset 152 may in some cases be the accessing of the data in the
computer memory 138. In other examples the receiving of the at
least one second magnetic resonance image dataset 152 may involve
the processor 134 controlling the magnetic resonance imaging system
102 to acquire the second magnetic resonance data 150 and then the
reconstruction of the second magnetic resonance data 150 into the
second magnetic resonance image dataset 152.
[0078] Next in step 204, the processor 134 receives an analysis
region 126 within both the first region of interest 122 and the
second region of interest 124. In some cases receiving the analysis
region 204 may involve receiving data from the user interface 136.
In other cases the receiving of the analysis region 126 may be
performed automatically by a registration algorithm that registers
the first magnetic resonance image dataset 146 to the at least one
second magnetic resonance image dataset 152 and determines what
data within the two image datasets overlaps and is in both
datasets.
[0079] Next in step 206, a cost function is constructed. The cost
function comprises an intra-scan homogeneity measure which is
calculated separately for the first magnetic resonance image
dataset and separately for each of the at least one second magnetic
resonance image dataset 152. The cost function further comprises an
inter-scan similarity measure calculated using both the first
magnetic resonance image dataset and each of the at least one
second magnetic resonance image dataset.
[0080] Next in step 208 the cost function is optimized by
calculating a first intensity correction map 154 for the first
magnetic resonance image dataset 146 and at least one second
intensity correction map 156 for each of the at least one second
magnetic resonance image dataset 152. Then in step 210 a first
corrected magnetic resonance data 158 is calculated by applying the
first intensity correction map 154 to the first magnetic resonance
image dataset 146. In step 212 at least one second corrected
magnetic resonance image 160 is calculated that is descriptive of
the analysis region using the at least one second magnetic
resonance image dataset 152 and the at least one second intensity
correction map 156.
[0081] FIG. 3 shows three sets of squares which tend to ideally
represent three different magnetic resonance image datasets. 152
and 152' represent two different second magnetic resonance image
datasets. A difficulty when comparing different magnetic resonance
images of the same subject is that there may be so called
bias-fields which result in inhomogeneity intensities across the
image. Particularly when the subject is placed into different
magnetic resonance imaging systems or the same magnetic resonance
imaging system at different points of time. It may be advantageous
to perform a so called longitudinal analysis of the various images
146, 152, 152'. Inhomogeneities in the intensity however may
prevent an automatic algorithm from functioning properly.
[0082] Examples may correct for this by performing an optimization
of a cost function. The cost function may have a variety of
different terms. There for example may be an intra-scan homogeneity
measure which is used to measure the intra-scan homogeneity of the
images 146, 152 and 152' separately. There may then be terms in the
cost function which make comparisons between the different images
146, 152 and 152'. For example there may be an inter-scan
similarity measure which compares image 146 and 152 and another
term which compares image 146 and 152'. In some examples there may
be additional terms which compare the second magnetic resonance
image datasets 152 and 152' with each other. Depending upon the
application each of these terms in the cost function may have
different weightings. These for example may be determined
empirically. The intra-scan homogeneity measure looks at the voxels
300 within a particular image 146, 152, 152'. The inter-scan
similarity measure may compare voxels 302 which correspond to each
other in the different images 146, 152, 152'.
[0083] Contrast in MRI may be widely affected by a bias-field, an
artifact of the MR acquisition process, which leads to
inhomogeneous intensities across the scan. Correction techniques
exist, but they either require prior knowledge about the expected
contrast, therefore limiting versatile applicability, or they are
limited in terms of accuracy and robustness. This invention
proposes a bias-field-correction technique for follow-up imaging
which not only increases homogeneity of tissue-specific contrast in
one scan but also increases the similarity of different registered
scans acquired at different time points, enabling improved
longitudinal assessment and quantification of brain scans. While a
bias-field, to some extent, might be easily compensated by human
perception during visual assessment, it generally affects
quantitative analysis since it alters intensity values.
[0084] Examples may provide for a means of bias field correction
that may be useful for follow-up imaging in which more than one
scan of the same subject is available (a longitudinal study).
Examples may exploits the fact that bias-field-corrected scans not
only should yield more homogeneous spatial contrast per scan, but
also should reveal consistent intensity distributions for different
time points.
[0085] The main element of some examples may include the
simultaneous estimation of the bias field of two or more scans of
the same subject by a joint optimization of (i) homogeneous
contrast per scan as well as (ii) a similar appearance of scans
acquired at different time points. It can be applied as an
extension to state-of-the-art techniques for bias-field correction
techniques such that multiple scans at once can be corrected
simultaneously.
[0086] Many approaches for bias-field-correction based on single
images model the bias-field as a multiplicative low-frequency
component, e.g. via b-splines with a limited number of control
points, or via other approaches such as DCT coefficients or
polynomial fields. The appearance of a bias-field is then optimized
via modification of their defining parameters while maximizing
intensity homogeneity. A widely used optimization criterion is
histogram sharpening, in which image intensities are assumed to
belong to mixed Gaussian distributions, while their standard
deviation indicate their inhomogeneity. For example, see: Tustison,
N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich,
P. A., Gee, J. C., 2010. N4ITK: improved N3 bias correction. IEEE
Trans. Med. Imaging 29, 1310-1320.
doi:10.1109/TMI.2010.2046908.
[0087] In the context of follow-up image quantification, the
optimization criterion not only can include separate components for
histogram sharpening for all longitudinal images but also the
homogeneity of their voxel-wise difference. All components, i.e.
intra-scan homogeneity constraints as well as inter-scan similarity
constraints might be combined, for example via linear combination
with generic, application-specific or custom weight factors.
[0088] For inter-subject similarity constraints, various
optimization constraints from the application domain of image
registration might be applicable, with a few examples given
below:
[0089] For two images of the same MR acquisition sequence,
inter-scan similarity constraint might be formulated as the
cross-correlation of two images or the voxel-wise sum of squared
differences between images.
[0090] For two images of a different MR acquisition sequence,
inter-scan similarity constraint might be formulated as the
maximized mutual information.
[0091] While the invention has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary and
not restrictive; the invention is not limited to the disclosed
embodiments.
[0092] Other variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed invention, from a study of the drawings, the
disclosure, and the appended claims. In the claims, the word
"comprising" does not exclude other elements or steps, and the
indefinite article "a" or "an" does not exclude a plurality. A
single processor or other unit may fulfill the functions of several
items recited in the claims. The mere fact that certain measures
are recited in mutually different dependent claims does not
indicate that a combination of these measured cannot be used to
advantage. A computer program may be stored/distributed on a
suitable medium, such as an optical storage medium or a solid-state
medium supplied together with or as part of other hardware, but may
also be distributed in other forms, such as via the Internet or
other wired or wireless telecommunication systems. Any reference
signs in the claims should not be construed as limiting the
scope.
LIST OF REFERENCE NUMERALS
[0093] 100 medical instrument [0094] 102 magnetic resonance system
[0095] 104 magnet [0096] 106 bore of magnet [0097] 108 imaging zone
[0098] 110 magnetic field gradient coils [0099] 112 magnetic field
gradient coil power supply [0100] 114 radio-frequency coil [0101]
116 transceiver [0102] 118 subject [0103] 120 subject support
[0104] 122 first region of interest [0105] 124 second region of
interest [0106] 126 analysis region [0107] 130 computer system
[0108] 132 hardware interface [0109] 134 processor [0110] 136 user
interface [0111] 138 computer memory [0112] 140 machine executable
instructions [0113] 142 pulse sequence commands [0114] 144 first
magnetic resonance data [0115] 146 first magnetic resonance image
data set [0116] 150 second magnetic resonance data [0117] 152
second magnetic resonance image data set [0118] 152' second
magnetic resonance image data set [0119] 154 first intensity
correction map [0120] 156 second intensity correction map [0121]
158 first corrected magnetic resonance image [0122] 160 second
corrected magnetic resonance image [0123] 300 voxels [0124] 302
voxel
* * * * *
References